Aiming at the problems of heterogeneous fog distribution and uneven illumination of underwater images, an image enhancement method is proposed based on global feature dual attention fusion generative adversarial network.Firstly, the convolutional layers are used instead of average pooling layers to continuously down-sample the input images and extract the global features.Secondly, the global feature dual attention fusion block is constructed, which is adaptive to the changing water environment and can enhance underwater information with various dissemination degrees more significantly.Finally, conditional information is incorporated as a restriction in training to increase the networks stability.The results of experiments demonstrate that the proposed algorithm outperforms the classic and latest algorithms and has fine functionality.